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Enhanced Zn anode kinetics and reversibility are achieved at a high ZUR by guiding Zn²⁺plating underlying the SnO_1.17interphase with a regulated (101) orientation, surpassing those achieved by inducing Zn(002) plating overlying the interphase. 

This work demonstrates the design protocols for high-energy-density solid-state Li–S batteries (SSLSBs). Also, it highlights the challenging issues for achieving practical SSLSBs towards the application in next-level electric transportation. 

Abstract Recent advancements in 3D printing technology have expanded its application to manufacturing pressure sensors. By harnessing the cost‐effectiveness, streamlined processes, and design flexibility of 3D printing, sensor fabrication can be customized to meet specific performance needs. Thus far, 3D printing in pressure sensor development has been primarily limited to creating molds for transferring patterns onto flexible substrates, restricting both material selection and sensor performance. To fully unlock the potential of 3D printing in advanced pressure sensor fabrication, it is crucial to establish effective design rules focused on enhancing the figure of merit performance. This study introduces a universal design strategy aimed at maintaining high sensitivity across a wide pressure range—a challenging feat, as sensitivity significantly decreases at higher pressures. Our approach centers on engineering the deformability of 3D‐printed structures, achieving a linear increase in contact area between sensor patterns and electrodes without reaching saturation. Sensors designed with high elongation and low stiffness exhibit consistent sensitivity of 162.5 kPa⁻¹ across a broad pressure range (0.05–300 kPa). Mechanistic investigations through finite element analysis confirm that engineered deformability is key to achieving this enhanced linear response, offering robust sensing capabilities for demanding applications such as deep‐sea and space exploration. 

Abstract Jeffbenite (Mg₃Al₂Si₃O₁₂) is a tetragonal phase found in so far only in superdeep diamonds, and its thermoelastic parameters are a prerequisite for determining entrapment pressures as it is regarded as a potential indicator for superdeep diamonds. In this study, the thermoelastic properties of synthetic Fe³⁺‐jeffbenite were measured up to 33.7 GPa and 750 K. High‐temperature static compression data were fitted, giving (∂K_T0/∂_T)_P = −0.0107 (4) GPa/K andα_T = 3.50 (3) × 10⁻⁵ K⁻¹. The thermoelastic properties and phase stability are applied to modeling isomekes, orP‐Tpaths intersecting possible conditions of entrapment in diamond. We calculate that under ideal exhumation, jeffbenite entrapped at mantle transition zone conditions will exhibit a high remnant pressure at 300 K (P_inc) of ∼5.0 GPa. Elastic geobarometry on future finds of jeffbenite inclusions can use the new equation of state to estimate entrapment pressures for this phase with still highly uncertain stability field in the mantle. 

By mimicking biomimetic synaptic processes, the success of artificial intelligence (AI) has been astounding with various applications such as driving automation, big data analysis, and natural-language processing.[1-4] Due to a large quantity of data transmission between the separated memory unit and the logic unit, the classical computing system with von Neumann architecture consumes excessive energy and has a significant processing delay.[5] Furthermore, the speed difference between the two units also causes extra delay, which is referred to as the memory wall.[6, 7] To keep pace with the rapid growth of AI applications, enhanced hardware systems that particularly feature an energy-efficient and high-speed hardware system need to be secured. The novel neuromorphic computing system, an in-memory architecture with low power consumption, has been suggested as an alternative to the conventional system. Memristors with analog-type resistive switching behavior are a promising candidate for implementing the neuromorphic computing system since the devices can modulate the conductance with cycles that act as synaptic weights to process input signals and store information.[8, 9] The memristor has sparked tremendous interest due to its simple two-terminal structure, including top electrode (TE), bottom electrode (BE), and an intermediate resistive switching (RS) layer. Many oxide materials, including HfO₂, Ta₂O₅, and IGZO, have extensively been studied as an RS layer of memristors. Silicon dioxide (SiO₂) features 3D structural conformity with the conventional CMOS technology and high wafer-scale homogeneity, which has benefited modern microelectronic devices as dielectric and/or passivation layers. Therefore, the use of SiO₂as a memristor RS layer for neuromorphic computing is expected to be compatible with current Si technology with minimal processing and material-related complexities. In this work, we proposed SiO₂-based memristor and investigated switching behaviors metallized with different reduction potentials by applying pure Cu and Ag, and their alloys with varied ratios. Heavily doped p-type silicon was chosen as BE in order to exclude any effects of the BE ions on the memristor performance. We previously reported that the selection of TE is crucial for achieving a high memory window and stable switching performance. According to the study which compares the roles of Cu (switching stabilizer) and Ag (large switching window performer) TEs for oxide memristors, we have selected the TE materials and their alloys to engineer the SiO₂-based memristor characteristics. The Ag TE leads to a larger memory window of the SiO₂memristor, but the device shows relatively large variation and less reliability. On the other hand, the Cu TE device presents uniform gradual switching behavior which is in line with our previous report that Cu can be served as a stabilizer, but with small on/off ratio.[9] These distinct performances with Cu and Ag metallization leads us to utilize a Cu/Ag alloy as the TE. Various compositions of Cu/Ag were examined for the optimization of the memristor TEs. With a Cu/Ag alloying TE with optimized ratio, our SiO₂based memristor demonstrates uniform switching behavior and memory window for analog switching applications. Also, it shows ideal potentiation and depression synaptic behavior under the positive/negative spikes (pulse train). In conclusion, the SiO₂memristors with different metallization were established. To tune the property of RS layer, the sputtering conditions of RS were varied. To investigate the influence of TE selections on switching performance of memristor, we integrated Cu, Ag and Cu/Ag alloy as TEs and compared the switch characteristics. Our encouraging results clearly demonstrate that SiO₂with Cu/Ag is a promising memristor device with synaptic switching behavior in neuromorphic computing applications. Acknowledgement This work was supported by the U.S. National Science Foundation (NSF) Award No. ECCS-1931088. S.L. and H.W.S. acknowledge the support from the Improvement of Measurement Standards and Technology for Mechanical Metrology (Grant No. 22011044) by KRISS. References [1] Younget al.,IEEE Computational Intelligence Magazine,vol. 13, no. 3, pp. 55-75, 2018. [2] Hadsellet al.,Journal of Field Robotics,vol. 26, no. 2, pp. 120-144, 2009. [3] Najafabadiet al.,Journal of Big Data,vol. 2, no. 1, p. 1, 2015. [4] Zhaoet al.,Applied Physics Reviews,vol. 7, no. 1, 2020. [5] Zidanet al.,Nature Electronics,vol. 1, no. 1, pp. 22-29, 2018. [6] Wulfet al.,SIGARCH Comput. Archit. News,vol. 23, no. 1, pp. 20–24, 1995. [7] Wilkes,SIGARCH Comput. Archit. News,vol. 23, no. 4, pp. 4–6, 1995. [8] Ielminiet al.,Nature Electronics,vol. 1, no. 6, pp. 333-343, 2018. [9] Changet al.,Nano Letters,vol. 10, no. 4, pp. 1297-1301, 2010. [10] Qinet al., Physica Status Solidi (RRL) - Rapid Research Letters, pssr.202200075R1, In press, 2022. 

Abstract Alkali-rich aluminous high-pressure phases including calcium-ferrite (CF) type NaAlSiO4 are thought to constitute ~20% by volume of subducted mid-ocean ridge basalt (MORB) under lower mantle conditions. As a potentially significant host for incompatible elements in the deep mantle, knowledge of the crystal structure and physical properties of CF-type phases is therefore important to understanding the crystal chemistry of alkali storage and recycling in the Earth’s mantle. We determined the evolution of the crystal structure of pure CF-NaAlSiO4 and Fe-bearing CF-NaAlSiO4 at pressures up to ~45 GPa using synchrotron-based, single-crystal X-ray diffraction. Using the high-pressure lattice parameters, we also determined a third-order Birch-Murnaghan equation of state, with V0 = 241.6(1) Å3, KT0 = 220(4) GPa, and KT0′ = 2.6(3) for Fe-free CF, and V0 = 244.2(2) Å3, KT0 = 211(6) GPa, and KT0′ = 2.6(3) for Fe-bearing CF. The addition of Fe into CF-NaAlSiO4 resulted in a 10 ± 5% decrease in the stiffest direction of linear compressibility along the c-axis, leading to stronger elastic anisotropy compared with the Fe-free CF phase. The NaO8 polyhedra volume is 2.6 times larger and about 60% more compressible than the octahedral (Al,Si)O6 sites, with K0NaO8 = 127 GPa and K0(Al,Si)O6 ~304 GPa. Raman spectra of the pure CF-type NaAlSiO4 sample shows that the pressure coefficient of the mean vibrational mode, 1.60(7) cm–1/GPa, is slightly higher than 1.36(6) cm−1/GPa obtained for the Fe-bearing CF-NaAlSiO4 sample. The ability of CF-type phases to contain incompatible elements such as Na beyond the stability field of jadeite requires larger and less-compressible NaO8 polyhedra. Detailed high-pressure crystallographic information for the CF phases provides knowledge on how large alkali metals are hosted in alumina framework structures with stability well into the lowermost mantle. 

Finite element analysis provides visual insights into conductive path evolution in a SiO₂-based memristor. Electrochemical impedance spectroscopy experimentally validated the theoretical findings by interpreting with an equivalent circuit. 

Memristors, a two-terminal device, have a resistance that can be changed and retained in two or more different states when subjected to electrical stresses. This unique function makes memristors now an attractive area of research for next-generation electronic devices such as memory and advanced computation. However, credible characterization methods for memristors are not fully established yet to understand fundamental working mechanisms and objectively evaluate figures of merit performance. This review encompasses various characterization methods from materials to electrical characteristics to identify the fundamentals of memristor operations. Meanwhile, large performance variation is the main bottleneck hindering the adoption of this class of devices in practical applications. Thus, the second part of this article focuses on the types of variation and other reliability issues of memristors. Possible strategies to enhance reliability are suggested as well. Topics covered in this review on memristors’ characterization techniques and reliability are of significant relevance to many studies that seek to advance the state of the art in electronic devices and systems towards neuromorphic computing. 

Organic solar cells (OSCs) using non-fullerene acceptors (NFAs) afford exceptional photovoltaic performance metrics, however, their stability remains a significant challenge. Existing OSC stability studies focus on understanding degradation rate-performance relationships, improving interfacial layers, and suppressing degradative chemical reaction pathways. Nevertheless, there is a knowledge gap concerning how such degradation affects crystal structure, electronic states, and recombination dynamics that ultimately impact NFA performance. Here we seek a quantitative relationship between OSC metrics and blend morphology, trap density of states, charge carrier mobility, and recombination processes during the UV-light-induced degradation of PBDB-TF:Y6 inverted solar cells as the PCE (power conversion efficiency) falls from 17.3 to 5.0%. Temperature-dependent electrical and impedance measurements reveal deep traps at 0.48 eV below the conduction band that are unaffected by Y6 degradation, and shallow traps at 0.15 eV below the conduction band that undergo a three-fold density of states increase at the PCE degradation onset. Computational analysis correlates vinyl oxidation with a new trap state at 0.25 eV below the conduction band, likely involving charge transfer from the UV-absorbing ZnO electron transport layer. In-situ integrated photocurrent analysis and transient absorption spectroscopy reveal that these traps lower electron mobility and increase recombination rates during degradation. Grazing-incidence wide-angle x-ray scattering and computational analysis reveal that the degraded Y6 crystallite morphology is largely preserved but that <1% of degraded Y6 molecules cause OSC PCE performance degradation by ≈50%. Together the detailed electrical, impedance, morphological, ultrafast spectroscopic, matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) spectroscopy, and computational data reveal that the trap state energies and densities accompanying Y6 vinyl oxidation are primarily responsible for the PCE degradation in these operating NFA-OSCs.